Inventors:
Maureen Caudill - San Diego CA
Jason Chun-Ming Tseng - Millbrae CA
Lei Wang - Carlsbad CA
Assignee:
Science Applications International Corporation - San Diego CA
International Classification:
G06F 1730
Abstract:
A relevancy ranking and clustering method and system that determines the relevance of a document relative to a users query using a similarity comparison process. Input queries are parsed into one or more query predicate structures using an ontological parser. The ontological parser parses a set of known documents to generate one or more document predicate structures. A comparison of each query predicate structure with each document predicate structure is performed to determine a matching degree, represented by a real number. A multilevel modifier strategy is implemented to assign different relevance values to the different parts of each predicate structure match to calculate the predicate structures matching degree. The relevance of a document to a users query is determined by calculating a similarity coefficient, based on the structures of each pair of query predicates and document predicates. Documents are autonomously clustered using a self-organizing neural network that provides a coordinate system that makes judgments in a non-subjective fashion.